New Technology in Agriculture:
Data and Methods to Overcome
Asymmetric Information
Will Masters
Friedman School of Nutrition, Tufts University
http://sites.tufts.edu/willmasters
NSF-AERC-IGC Workshop on Agriculture and Development
December 3, 2010 • Mombasa, Kenya
New Technology in Agriculture:
What can explain these huge differences in yield (and TFP?)?
USDA estimates of average cereal grain yields (mt/ha), 1960-2010
4.5
4.0
3.5
3.0
Rest-of-World
World
Southeast Asia
South Asia
Sub-Saharan Africa
2.5
2.0
1.5
1.0
0.5
0.0
Source: Calculated from USDA , PS&D data (www.fas.usda.gov/psdonline), downloaded 7 Nov 2010. Results shown are
each region’s total production per harvested area in barley, corn, millet, mixed grains, oats, rice, rye, sorghum and wheat.
New Technology in Agriculture:
What can explain these huge differences in yield (and TFP?)?
• The old literature is still relevant!
– Induced innovation and collective action in response to factor scarcity
– Political economy of support for agriculture, commitment to R&D etc.
– Rates of return, incidence of benefits and market structure
– Adoption and behavior (commitment, learning, discounting, risk etc.)
• Something new to consider:
– Asymmetric information between funders and R&D agencies
– The resulting insights could help explain other rates of innovation
New Technology in Agriculture:
Data and Methods to Overcome Asymmetric Information
A one-slide summary:
• Motivation (stylized facts about agricultural innovation)
–
–
–
–
technologies are location-specific, tailored to agroecological conditions
benefits are largely non-excludable, spread among consumers & users
benefits are difficult to distinguish from other trends or shocks
benefits remain consistently very large, with persistent underinvestment
• Diagnosis (one of many potentially relevant models)
– an Akerlof (1970) ‘market for lemons’
– R&D is a credence good, difficult for investors/funders to buy
• Remedies (interventions to be tested)
– procurement only from trusted brand (e.g. CGIAR, universities), or…
– third-party certification to reveal performance data
• impact assessments and case studies
• technology contests and prizes for disclosure
Motivation:
Technologies must be tailored to local agro-ecologies
Regions differ in their technology lags; a classic example is:
Motivation:
Technologies must be tailored to local agro-ecologies
Here is some modern data on a somewhat similar technology lag:
Source: Reprinted from W.A. Masters, “Paying for Prosperity: How and Why to Invest in Agricultural
Research and Development in Africa” (2005), Journal of International Affairs, 58(2): 35-64.
Motivation:
Benefits are diffuse and hard to attribute, but very large
Source: J.M. Alston, M.C. Marra, P.G. Pardey & T.J. Wyatt (2000). Research returns redux: A meta-analysis of
the returns to agricultural R&D. Australian Journal of Agricultural and Resource Economics, 44(2), 185-215.
Motivation:
Investment rates stable and falling,
despite high estimated rates of return
Reprinted from Philip G. Pardey, Nienke Beintema, Steven Dehmer, and Stanley Wood (2006), “Agricultural
Research: A Growing Global Divide?” Food Policy Report No. 17. Washington, DC: IFPRI.
Diagnosis:
Why is there persistent underinvestment?
• Why need public R&D at all – why not just IPRs ?
– enforcement is prohibitively expensive for many technologies
– e.g. in genetic improvement, contrast maize vs. soy vs. wheat & rice
• Why would public R&D be unresponsive to impact data?
– this could be a generic collective-action failure, but also specifically…
– ag. technology performance data are private and location-specific;
R&D project selection and supervision is particularly difficult
• One aspect of this problem is Akerlof’s ‘market for lemons’
– Investment is constrained by trust (R&D is a credence good)
– Without trust, investment level would be zero
The investments we see occur via only the most trusted institutions
Remedies: How can funders target their R&D investments?
• What are the (more or less) trusted R&D agencies we see?
– IARCs: core funding through CGIAR, plus donor-funded projects
– NARIs: core funding from host govts, plus donor-funded projects
– Donor-country institutions: core funding varies, plus projects
• Can third-party certification overcome info. asymmetry?
– Who does evaluation and impact assessments?
– What do they find?
Selected results from Alston et al. (2000) meta-analysis for rate of return estimates (n=1,128)
Slide 11
Remedies: How can funders target their R&D investments?
• Trusted brands
– IARCs: core funding through CGIAR, plus donor-funded projects
– NARIs: core funding from host govts, plus WB loans and projects
– Donor-country universities: core funding varies, plus projects
• Third-party certification
– Who does evaluation and impact assessments?
– What do they find?
• Consistently high payoffs, self-evaluations actually show lower returns
• Can the new wave of evaluation research help?
– Are RCTs appropriate?
• Yes, but…
• Not for R&D itself [national-scale programs, non-excludable impacts]
– For this, we have pull mechanisms...
• A long history with important new twists
Pull mechanisms: the long history of philanthropic prizes
(shown here: 1700-1930)
Net present value of
prizes paid
French Academy of Sciences
Montyon prizes for medical challenges
(2006 US dollars,
not to scale)
$51,118,231
Deutsch Prize for flight between the
$12,600,000
Aero-Club de France and Eiffel Tower
British Longitude prize for
determining longitude at sea
The Daily Mail prize for flight
$5,997,097
across the English Channel
$3,364,544
French government prize for
food preservation techniques
$1,045,208
Hearst prize for crossing
continental US in 30 day
French government prize for
large scale hydraulic turbine
French government prize $421,370
for producing alkali soda
$644,203
Milan Committee prize
for flight across Alps
$618,956
$582,689
The Daily Mail prize for transatlantic flight
$515,770
$289,655
Chicago Times-Herald prize for motors for
self-propelling road carriage
$123,833
Scientific American prize for first plane in US to fly 1 km
$56,502
Wolfskehl prize for proof of Fermat’s Last Theorem
1700
1750
1800
1850
Orteig prize for solo
flight NY to Paris
1900
$31,690
1930
Pull mechanisms: an explosion of new interest
(shown here: 1930-2009)
Net present value of prizes paid
(2006 US dollars, not to scale)
Advance market Commitment for pneumococcal disease vaccine up to $1.5 billion
Soviet Incentive Awards
For Innovative Research
1930
Bigelow Space Prize for crew transport into orbit $ 50,000,000
Super Efficient Refrigerator Program for
highly efficient CFC free refrigerator
$165,755,396
$37,682,243
Virgin Earth Challenge for removal of greenhouse gases $ 25,000,000
European Information and Communication Technology Prize
$ 10,917,192
Ansari X Prize for private manned space flight $ 10,717,703
Archon X Prize for sequencing the human genome $ 10,000,000
$7,000,000
Millennium Math Prizes for seven unsolved problems
DARPA Grand Challenge for robotics in vehicles
$6,660,406
$6,000,000
Lemelson-MIT Prize for invention of a patented product useful to society
$4,300,000
Methuselah Mouse Prize for demonstration of slowing of ageing process on mouse
NASA Centennial Challenges for Improvements in space exploration
$2,000,000
$1,882,290
Schweighofer Prize for Europe’s forest industry competitiveness
$1,600,000
International Computer Go Championship
$1,210,084
Budweiser Challenge for first non-stop balloon flight around the globe
$1,210,084
Rockefeller Foundation Prize for Rapid STD Diagnostic Test
$1,210,084
Grainger Challenges for development of economical filtration devices for the
removal of arsenic from well water in developing countries
$588,092
Kremer Prize for Human Powered Flight
Across the English Channel
Kremer Prize for Human
Powered Flight (Figure 8)
$290,153
CATS Prize for inexpensive commercial launch of payload into space
$654,545
Feynman Prizes for nano
tech robot technology
$250,000
$250,000
Electronic Frontier Foundation Cooperative Com$50,000-250,000
puting Challenge for new large prime numbers
Beal’s
$128,489
Fredkin Prize for Chess Computer Program
Conjecture Prize
$100,000
Loebner Prize for Computer that
can pass the Turing Test
$100,000
Polytechnische Gesellscaft Prize
for Human Powered Flight
$59,240
1940
Goldcorp Challenge for best gold
prospecting methods or estimates
1950
1960
1970
1980
1990
2000
Pull mechanisms are prize contests;
can offer very high-powered incentives
• Successful prize contests offer:
– an achievable target, an impartial judge, credible commitment to pay
• Such prizes elicit a high degree of effort:
– Typically, entrants collectively invest much more than the prize payout
– Sometimes, individual entrants invest more than the prize
• e.g. the Ansari X Prize for civilian space travel offered to pay $10 million
• the winners, Paul Allen and Burt Rutan, invested about $25 million
• Why do prizes attract so much investment?
– contest provides a potentially valuable signal of success
– value of the signal depends on degree of previous market failure
• the X Prize winners licensed designs to Richard Branson for $15 million
• and eventually sold the company to Northrop Grumman for $??? million
• total public + private investment in prize-winning technologies ~ $1 billion
…but traditional prize contests
have serious limitations!
• Traditional prize contests are winner-take-all (or rank-order)
– this is inevitable when only one (or a few) winners are needed, but...
• Where multiple successes could coexist, imposing winner-take-all
payoffs introduces inefficiencies
– strong entrants discourage others (paper forthcoming in J.Pub. E.)
• potentially promising candidates will not enter
– pre-specified target misses other goals
• more (or less) ambitious goals are not pursued
– focusing on few winners misses other successes
• characteristics of every successful entrant might be informative
• New incentives can overcome these limitations with more
market-like mechanisms, that have many winners
New pull mechanisms
allow for many winners
• From health and education, two examples:
– pilot Advance Market Commitment for pneumococcal disease vaccine
• launched 12 June 2009, with up to $1.5 billion, initially $7 per dose
– proposed “cash-on-delivery” (COD) payments for school completion
• would offer $200 per additional student who completes end-of-school exams
• What new incentive would work for agriculture?
– what is the desired outcome?
• unlike health, we have no silver bullets like vaccines
• unlike schooling, we have no milestones like graduation
• instead, we have on-going adoption of diverse innovations in local niches
– what is the underlying market failure?
• for AMC and COD, the main market failure is commitment failure
• for agricultural R&D, the main market failure is asymmetric information
What new incentives could best
reward new agricultural technologies?
• New techniques from elsewhere did not work well in Africa
– local adaptation has been needed to fit diverse niches
– new technologies developed in Africa are now spreading
• Asymmetric information limits scale-up of successes
– local innovators can see only their own results
– donors and investors try to overcome the information gap with project selection,
monitoring & evaluation, partnerships, impact assessments…
– but outcome data are rarely independently audited or publically shared
• The value created by ag. technologies is highly measureable
– gains shown in controlled experiments and farm surveys
– data are location-specific, could be subject to on-side audits
• So donors could pay for value creation, per dollar of impact
– a fixed sum, divided among winners in proportion to measured gains
– like a prize contest, but all successes win a proportional payment
Proportional prizes complement
other types of contest design
Target is
pre-specified
Success is ordinal
(yes/no, or
rank order)
Success is cardinal
(increments can
be measured)
Target is to be
discovered
Most technology prizes
(e.g. X Prizes)
Achievement awards
(e.g. Nobel Prizes, etc.)
AMC for medicines,
COD for schooling
(fixed price per unit)
Proportional prizes
(fixed sum divided in
proportion to impact)
How proportional prizes would work
to accelerate innovation
• Donors offer a given sum (e.g. $1 m./year), to be divided among all
successful new technologies
• Innovators assemble data on their technologies
– controlled experiments for output/input change
– adoption surveys for extent of use
– input and output prices
• Secretariat audits the data and computes awards
• Donors disburse payments to the winning portfolio of techniques, in
proportion to each one’s impact
• Investors, innovators and adopters use prize information to scale up
spread of winning techniques
Implementing Proportional Prizes:
Data requirements
Data needed to compute each year’s
economic gain from technology adoption
Price
D
S
S’
S”
Variables and data sources
J (output gain)
P
K
(cost reduction)
ΔQ
Field data
Yield change ×adoption rate
J
Input change per unit
I
I
Economic parameters
Supply elasticity (=1 to omit)
K
Δ Q Demand elasticity (=0 to omit)
(input change)
Q
Market data
P,Q National ag . stats.
Q’
Quantity
Implementing Proportional Prizes:
Data requirements
Data needed to impute each year’s
adoption rate
Fraction of
surveyed
domain
Other survey
(if any)
First
survey
Projection (max. 3 yrs.)
Linear
interpolations
First release
Application date
Year
Implementing Proportional Prizes:
Data requirements
Calculation of NPV over past and future years
Discounted
Value
(US$)
“Statute of
limitations”
(max. 5 yrs.?)
First release
Projection
period
(max. 3 yrs.?)
Year
NPV at application date,
given fixed discount rate
Implementing Proportional Prizes:
Hypothetical results of a West African contest
Example results using case study data
Example technology
1. Cotton in Senegal
Measured
Social Gains
(NPV in US$)
Measured
Social Gains
(Pct. of total)
Reward
Payment
(US$)
14,109,528
39.2%
392,087
2. Cotton in Chad
6,676,421
18.6%
185,530
3. Rice in Sierra Leone
6,564,255
18.2%
182,413
4. Rice in Guinea Bissau
4,399,644
12.2%
122,261
5. “Zai” in Burkina Faso
2,695,489
7.5%
74,904
6. Cowpea storage in Benin
1,308,558
3.6%
36,363
231,810
0.6%
6,442
$35.99 m.
100%
$1 m.
7. Fish processing in Senegal
Total
Note: With payment of $1 m. for measured gains of about $36 m., the implied
royalty rate is approximately 1/36 = 2.78% of measured gains.
Implementing Proportional Prizes:
Opportunity for a single-country trial in Ethiopia
New technology adoption is stalled:
Share of cropped area under new seeds for major cereal grains, 1996-2008
Source: Ethiopian Central Statistical Agency data, reprinted from D.J. Spielman, D. Kelemework and D. Alemu (forthcoming), “Seed, Fertilizer,
and Agricultural Extension in Ethiopia.” Draft chapter for P. Dorosh, S. Rashid, and E.Z. Gabre-Madhin, eds., Food Policy in Ethiopia.
Implementing Proportional Prizes:
Opportunity for a single-country trial in Ethiopia
Adoption is especially slow for seeds:
Number and proportion of farm holders applying new inputs, by education
Proportion of farms using new inputs:
No. of farms
Fert.
Impr. Seed
Pesticide
Irrigation
12,916,120
44%
12%
24%
8%
Illiterate
8,239,615
41%
10%
22%
8%
Informally educated
1,016,284
48%
13%
23%
12%
Some formal education
3,660,222
51%
16%
30%
8%
All farm holders
Of whom:
Source: Author's calculations, from CSA (2010), “Agricultural Sample Survey 2009-2010 (2002 E.C),
Meher Season.” Version 1.0, 21 July 2010. Addis Ababa: Central Statistical Authority of Ethiopia.
Available online at http://www.csa.gov.et/index.php?&id=59.
In conclusion….
Back to the intro:
• The old literature is still relevant!
– Induced innovation and collective action in response to factor scarcity
– Political economy of support for agriculture, commitment to R&D etc.
– Rates of return, incidence of benefits and market structure
– Adoption and behavior (commitment, learning, discounting, risk etc.)
• Something new to consider:
– Asymmetric information between funders and R&D agencies
– The resulting insights could help explain other rates of innovation